estep {mclust1998} | R Documentation |
E-step for estimating conditional probabilities from parameter estimates in an MVN mixture model having possibly one Poisson noise term.
estep(data, modelid, mu, ...)
data |
matrix of observations. |
modelid |
An integer specifying a parameterization of the MVN covariance matrix
defined by volume, shape and orientation charactertistics of the
underlying clusters. The allowed values for modelid and their
interpretation are as follows: "EI" : uniform spherical,
"VI" : spherical, "EEE" : uniform variance, "VVV" :
unconstrained variance, "EEV" : uniform shape and volume,
"VEV" : uniform shape.
|
mu |
matrix whose columns are the Gaussian group means. |
... |
additional arguments, as follows:
sigmasq - spherical models) or covariances
(sigma - elliposidal models)
prob is
missing, the number of groups is assumed to be the number of columns
in mu (no noise). A Poisson noise term will appear in the
conditional probabilities if length(prob) is equal to
ncol(mu)+1 .
eps varies the parameterization, each of
which has a default.
prob indicates a noise term). Default : determined by function
hypvol .
|
the conditional probablilities corresponding to the parameter estimates. The loglikelihood is returned as an attribute.
The reciprocal condition estimate returned as an attribute ranges in value between 0 and 1. The closer this estimate is to zero, the more likely it is that the corresponding EM result (and BIC) are contaminated by roundoff error.
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley (1997).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid="VI"), 3) z <- me( iris[,1:4], ctoz(cl), modelid = "VI") pars <- mstep( iris[,1:4], modelid = "VI", z) estep(iris[,1:4], modelid = "VI", pars$mu, pars$sigma, pars$prob)